Results 91 to 100 of about 16,533 (261)

Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks

CoRR
This article introduces Perception-Informed Neural Networks (PrINNs), a framework designed to incorporate perception-based information into neural networks, addressing both systems with known and unknown physics laws or differential equations. Moreover, PrINNs extend the concept of Physics-Informed Neural Networks (PINNs) and their variants, offering a
Mehran Mazandarani, Marzieh Najariyan
openaire   +2 more sources

Solution‐Processed Two‐Dimensional Indium Oxide on Sodium‐Embedded Alumina for Reconfigurable Optoelectronic Synaptic Transistors

Advanced Functional Materials, EarlyView.
Wafer‐scale two‐dimensioanl In2Se3 oxidized into InOx on sodium‐embedded beta‐alumina enables multifunctional reconfigurable electronics. Sodium ions accumulate within distinct spatial distribution under drain‐controlle and gate‐controlled operation. Drain‐control operation gives controllability of ultraviolet‐driven optoelectronic synaptic conductance
Jinhong Min, Yonghyun Albert Kwon, Hyunbin Kim, Jihyun Kim, Myeongjin Jung, Jaewon Heo, Gwang Ya Kim, Vlastimil Mazanek, Zdeněk Sofer, In Soo Kim, Jeong Ho Cho, Hyesung Park, Sang Min Won, Joohoon Kang   +13 more
wiley   +1 more source

Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

Advanced Functional Materials, EarlyView.
A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou, Yunsik Ohm, Ren‐Mian Chin, Olivia Kerr, Krithika Manohar, Mohammad H. Malakooti   +5 more
wiley   +1 more source

Space Correlation Constrained Physics Informed Neural Network for Seismic Tomography

Journal of Geophysical Research: Machine Learning and Computation
Physics‐informed neural networks (PINNs) integrate physical constraints with neural architectures and leverage their nonlinear fitting capabilities to solve complex inverse problems.
Yonghao Wang, Zhuo Jia, Bowu Jiang, Wenkai Lu   +3 more
doaj   +1 more source

Integrated Field‐Free SOT Domain‐Wall Synapses and MTJ Stochastic Neurons for Hardware Boltzmann Machines

Advanced Functional Materials, EarlyView.
Field‐free spin‐orbit torque domain‐wall synapses integrated with stochastic MTJ neurons enable compact hardware Boltzmann machines. Leveraging intrinsic stochasticity and multi‐level conductance, the system achieves efficient probabilistic learning with high accuracy, demonstrating a scalable spintronic platform for energy‐efficient edge AI.
Aijaz H. Lone, Meng Tang, Camelia Florica, Bin He, Jingkai Xu, Selma Amara, Georgian Melinte, Xixiang Zhang, Gianluca Setti   +8 more
wiley   +1 more source

A Physics‐Informed Neural Network Approach to the Gannon Storm

Geophysical Research Letters
Extreme geomagnetic storms, such as the May 2024 Gannon event, pose significant risks to technological infrastructure, requiring robust forecasting models.
M. Lacal, E. Camporeale, M. Piersanti, G. Consolini   +3 more
doaj   +1 more source

Swelling‐Induced Stress‐Assisted Transfer of Nanodiamond Arrays With a PVA Carrier Tape for Conformal Bio‐Integrated Sensing and Labelling

Advanced Functional Materials, EarlyView.
This work introduces a swelling‐induced, stress‐assisted water‐soluble PVA tape strategy to transfer‐print nanodiamond quantum‐sensor arrays onto soft, curved biological interfaces. The room‐temperature, water‐triggered process achieves >98% fidelity and residue‐free integration, enabling conformal quantum sensing on contact lenses, neural probes, and ...
Luyao Zhang, Lingzhi Wang, Xinhao Hu, Tai Nam Yip, Mingze Sun, Jixiang Jing, Lizhi Xu, Yuan Lin, Yong Hou, Zhiqin Chu   +9 more
wiley   +1 more source

Backbone‐Controlled Ion‐Side Chain Accessibility in Conjugated Polymers for Organic Electrochemical Synaptic Transistors

Advanced Functional Materials, EarlyView.
Backbone modulation in glycolated conjugated polymers governs ion accessibility to side chains, strengthes anion adsorption, and suppresses back‐diffusion. As the number of thiophene units increases, structural reorganization, retention, and synaptic plasticity are enhanced, leading to improved neuromorphic performance in electrolyte‐gated organic ...
Junho Sung, Sein Chung, Byeongjun Jeon, Donghwa Lee, Myungjin An, Jiyeong Shin, Yoohyeon Jang, Donghoo Won, Changduk Yang, Bumjoon Seo, Eunho Lee   +10 more
wiley   +1 more source

Quantifying Subsurface Weak in‐Plane Magnetization of Mixed Phase BiFeO3 by Scanning Nitrogen Vacancy Magnetometry

Advanced Functional Materials, EarlyView.
We use scanning nitrogen vacancy magnetometry to directly image the weak in‐plane magnetic moments in mixed phase BiFeO3 at the nanoscale and quantify the local magnetic moments to be 18.8±2.0 μB/nm2 in the rhombohedral‐like phase and 1.5±0.6 μB/nm2 in the well‐known non‐magnetic tetragonal‐like phase.
Lei Wang, Zhewen Xu, Yuwen Xu, Björn Josteinsson, Simon Josephy, Dawei Zhang, Yunlong Sun, Heung‐Sik Park, Jihun Kim, Kanghyun Chu, Kyung Song, Danyang Wang, Chan‐Ho Yang, Andrea Morales, Jan Seidel   +14 more
wiley   +1 more source

SPIKANs: separable physics-informed Kolmogorov–Arnold networks

Machine Learning: Science and Technology
Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing.
Bruno Jacob, Amanda A Howard, Panos Stinis   +2 more
doaj   +1 more source